hide
Free keywords:
-
Abstract:
Soil moisture remote sensing data and hydrological modeling can be combined through data assimilation to provide a comprehensive representation of hydrological states and fluxes. However, uncertainties in modeling and measurements can limit the accuracy of the data assimilation. Therefore, conducting a comprehensive analysis of remotely sensed, modeled, and in-situ soil moisture is crucial to quantitatively assess both systematic and random errors.. Four remotely sensed soil moisture products will be evaluated: the Soil Moisture and Ocean Salinity (SMOS)-L3, the Soil Moisture Active Passive (SMAP)-L3 , the ESA CCI soil moisture, and the Advanced Microwave Scanning Radiometer-2 (AMSR-2). Those are compared to simulations by the Terrestrial System Modeling Platform (TSMP), a scale-consistent, highly modular, massively parallel, fully integrated soil-vegetation-atmosphere modeling system. In-situ reference measurements are obtained by the International Soil Moisture Network (ISMN). Different strategies for assimilation are evaluated: no pre-processing, bias removal in the observations, Savitzky-Golay filtering to remove random noise, range scaling, and cumulative density function (CDF) matching procedure. A comprehensive statistical analysis will be conducted to evaluate the performance of the remotely sensed soil moisture products, including the calculation of key metrics such as the root mean square deviation (RMSD), unbiased RMSD, bias, Pearson correlation coefficient, and Spearman's rank correlation coefficient (Spearman's rho). The analysis will be performed for four sites of TERENO network for dry, wet, and normal years. These resulting reanalysis will offer a potent tool for expanding our knowledge of soil moisture dynamics and assisting in the creation of more efficient land and water management techniques.
